Video-Based Pig Recognition with Feature-Integrated Transfer Learning

Abstract

Automatic detection and recognition of animals has long been a popular topic. It can be used on different areas, such as ecosystem protection, farming industry, insurance industry, etc. Currently, there is still no robust and efficient method for this problem. Deep neural network, a recently rapid developing technology, has shown its great power on image processing, but suffers from low training speed problem. Recently, transfer learning has become popular because it avoids training the network from scratch, which significantly speeds up the training speed. In this paper, we focus on the pig recognition contest organized by a Chinese finance company. Applying all frames for training the neural networks with VGG-19 will result in an accuracy lower than 60% in the prediction steps. With experiments, we find out a key to enhance the accuracy of the video-based pig recognition task is that the frames have to be carefully selected with a certain algorithm. To take advantage of the strengths of different network architectures, we apply feature integration method with the deep neural networks of DPN131, InceptionV3 and Xception network together. We then implement the integrated feature to train the labelled dataset which are frames extracted from the videos of 30 pigs. The resulted model receive an prediction accuracy of 96.41%. Experiments show that the best performance of our proposed methods outperforms all classic deep neural networks training from scratch.